Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA

We study how a regulator can best target inspections. Our case study is a US Occupational Safety and Health Administration (OSHA) program that randomly allocated some inspections. On average, each inspection led to 2.4 (9 percent) fewer serious injuries over the next five years. Using new machine learning methods, we find that OSHA could have averted as much as twice as many injuries by targeting inspections to workplaces with the highest expected averted injuries and nearly as many by targeting the highest expected level of injuries. Either approach would have generated up to $850 million in social value over the decade we examine.

JEL Classification: C63, J28, J81, K32, L51

Suggested Citation: Suggested Citation

Johnson, Matthew and Levine, David Ian and Toffel, Michael W., Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA (October 1, 2023). Harvard Business School Technology & Operations Mgt. Unit Working Paper No. 20-019, Available at SSRN: https://ssrn.com/abstract=3443052 or http://dx.doi.org/10.2139/ssrn.3443052